基于改进U-Net的全心脏CT图像分割

Whole-heart CT Image Segmentation Based on Improved U-Net

针对CT图像中全心脏结构复杂度高、分割不完整及分割精度低等问题,文章提出了一种改进U-Net的全心脏分割方法。根据全心脏结构形态特点,文章将多并行尺度特征融合模块引入U-Net网络的编码层,并在U-Net网络的跳层连接中加入了注意力机制。文章利用MM-WHS数据集将改进的全心脏分割算法在中南民族大学认知科学实验室中进行了一系列的全心脏分割实验。实验结果显示,文章提出的算法分割相似度达到88.73%,提高了全心脏结构的分割准确率。

Aiming at the problems of high structural complexity,incomplete segmentation and low segmentation accuracy of whole heart in CT images,the paper proposes a whole-heart segmentation method based on improved U-Net.According to the structural and morphological characteristics of the whole heart,multi-parallel scale feature fusion module is introduced into the coding layer of U-Net network,and attention mechanism is added into the skipping layer of U-Net network.A series of whole heart segmentation experiments are carried out in the Cognitive Science Laboratory of South-Central University for Nationalities using the improved whole heart segmentation algorithm based on the MM-WHS dataset.Experimental results show that the similarity of the proposed algorithm reaches 88.73%,which improves the segmentation accuracy of the whole heart structure.